MIMO (Multiple-Input Multiple-Output) is available as a wireless communication technology. In the MIMO technology, different signals are transmitted from multiple transmit antennas in parallel and are spatially multiplexed to achieve high-speed transmission.
In such MIMO wireless communication systems, a receiving end performs various types of detection processing so that it can as accurately as possible demultiplex and detect the signals transmitted from the transmit antennas.
One example of the detection processing is Full-MLD (Maximum Likelihood Detection). Full-MLD is detection processing in which, for example, distances between reception signal points and transmitted candidate points (or signal replica candidate points) are determined and the transmitted candidate points with which the distance is the smallest are estimated as transmission signal points. In Full-MLD, however, since distances with respect to all transmitted candidate points are computed, the amount of computation becomes enormous depending on the number of transmit antennas, a modulation system, and so on. Accordingly, detection processing called QRM-MLD has also been available.
QRM-MLD is a combination of, for example, QR decomposition and MLD to estimate the transmission signal points while reducing the transmitted candidate points. Thus, QRM-MLD involves a small amount of computation compared to Full-MLD. QRM-MLD will now be described.
First, a MIMO wireless communication system is modeled, for example, as follows:y=Hx+n  (1)
In equation (1), y denotes a reception signal vector, x denotes a transmission signal vector, n denotes a noise vector, and H denotes a channel response matrix (or a channel matrix). When the number of transmit antennas is 2 and the number of receive antennas is 2, equation (1) can be expressed as:
                              (                                                                      y                  0                                                                                                      y                  1                                                              )                =                                            (                                                                    a                                                        b                                                                                        c                                                        d                                                              )                        ⁢                          (                                                                                          x                      0                                                                                                                                  x                      1                                                                                  )                                +                      (                                                                                n                    0                                                                                                                    n                    1                                                                        )                                              (        2        )            
In equation (2), y0 and y1 denote reception signal points, x0 and x1 denote transmission signal points (or transmission-signal candidate points), a, b, c, and d denote elements of the channel matrix H, and n0 and n1 denote noise components.
In this case, the channel matrix H can be decomposed into a unitary matrix Q (a matrix whose matrix product with a complex conjugate transpose matrix Q* is equal to a unit matrix) and an upper triangular matrix R and can be expressed by QR decomposition as:H=QR  (3)
Multiplying both sides of equation (1) from the left by the complex conjugate transpose matrix Q* of the unitary matrix Q yields:
                                                                                          Q                  *                                ⁢                y                            =                            ⁢                                                Q                  *                                ⁡                                  (                                      Hx                    +                    n                                    )                                                                                                        =                            ⁢                                                                    Q                    *                                    ⁢                  Hx                                +                                                      Q                    *                                    ⁢                  n                                                                                                        =                            ⁢                              Rx                +                                  n                  ′                                                                                        (        4        )            
Therefore, equation (4) can be expressed as:
                              (                                                                      y                  0                  ′                                                                                                      y                  1                  ′                                                              )                =                                            (                                                                                          a                      ′                                                                                                  b                      ′                                                                                                            0                                                                              c                      ′                                                                                  )                        ⁢                          (                                                                                          x                      0                                                                                                                                  x                      1                                                                                  )                                +                      (                                                                                n                    0                    ′                                                                                                                    n                    1                    ′                                                                        )                                              (        5        )            
In this case, y0′ and y1′ denote signal points obtained by multiplying the reception signal points y0 and y1 by the unitary matrix Q, a′, b′, and c′ denote elements of the upper triangular matrix R, and n0′ and n1′ denote values obtained by multiplying the noise components n0 and n1 by the unitary matrix Q. The elements of equation (5) are given as:y0′=a′x0+b′x1+n0′  (5-1)y1′=c′x1+n1′  (5-2)
In QRM-MLD, the candidate points having a smallest amount of noise, i.e., x0 and x1 with which expression (5-3) below is the smallest, are selected from the candidate points x0 and x1:|y1′−c′x1|2+|y0′−a′x0−b′x1|2  (5-3)
That is, in a first stage, multiple candidate points x1 with which |y1′−c′x1|2 is smaller than a threshold are selected, and in a second stage, candidate points x0 with which |y0′−a′x0−b′x1|2 is smaller than a threshold are selected from the multiple candidate points x1 selected in the first stage. Lastly, the candidate points x0 and x1 with which expression (5-3) is the smallest are selected from those selected candidate points x0 and x1 and are determined (estimated) to be the transmission signal points x0 and x1.
For example, there has been a MIMO multiplexing communication apparatus in which, in a stage M+1, branch metrics (Euclidean distances) with respect to symbol replica candidates are compared with a threshold, and when the branch metric is larger than the threshold, subsequent search is not performed.
One example is a receiver that is adapted to derive a relative signal-to-noise ratio for each set of a modulation system and a code rate by using a determination table, to rearrange the elements of the channel matrix in descending order of the signal-to-noise ratio, and to sequentially estimate the transmission signals in ascending order of the error rates of the reception signals.
Another example is a signal detecting apparatus that is adapted to narrow down candidates of the transmission sequence, excluding upper-limit-exceeding cumulative metrics of cumulative metrics determined in cumulative-metrics generation processing and partial layer sequence candidates corresponding to the upper-limit exceeding cumulative metrics from the candidates.